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Improving Invoicing Lead Time with Process Analysis

Invoicing lead time is basically quite a simple KPI: If you sell tangible or intangible goods, you measure time from the goods issued or service provided to the invoice being sent.

Okay, so if it’s simple as that, then what do we need Process Analysis for? Well… There might just be solid business benefits in also finding out how much time passes until the sent invoices are in fact paid. And maybe even see if there are credit memos being issued.

Without Process Analysis, you do not easily see the combination of these factors:

From analyzing accounts payable processes, we have noticed that if you want to have your accounts receivables in as soon as possible, sending out the invoices as soon as humanly possible is not the optimal solution. For more details, see previous blog post and webinar on cash discount utilization. Almost all large companies have automated their AP processes as much as possible. They rely heavily on a three-way-matching: if what was ordered was delivered and invoiced accurately, the invoice is paid automatically without any human interaction.

Unfortunately, three-way-matching fails whenever the invoice reaches the system before the goods have been (or even can be) received. And not all incoming invoice handling system can even re-try the match on a later date, but a human interaction is needed, which takes time.

When the business is not all about tangible products but also contains service, things will get more complicated.

We can take Caverion, a QPR customer, as a real-life example. Caverion is a building service company that noticed their cash flow was slowing down. By using QPR ProcessAnalyzer, it was found that the slowed cash flow was not due to diminishing sales, but because the invoices were kept in a pending state long after the actual delivery of the service. For this issue, QPR ProcessAnalyzer was a pivotal tool. It enabled Caverion to match their worktime reporting to the invoicing. The problem started sorting itself out after time from “Work Completion” and “Invoice Created” was monitored and reported. QPR ProcessAnalyzer could tell the problematic cases easily.

Analysis of commonalities between late cases resulted then in new reports where timelines of hour entries made by the employees were monitored on a weekly basis. From this effort, work time entry accuracy was improved.

Of course, things do not need to end here. We could take the historical process data, enable machine learning and start predicting if a case is likely to create delays in the invoicing stage. We could prevent most of the problematic cases even from emerging and thus minimize the total number of problematic cases. This way, problems could be solved before they become monetary issues.